Seeing is believing: Using Crop Pictures in Personalized
Advisory Services

Policymakers in many low- and middle-income countries have tried to promote affordable, effective risk management instruments, such as crop insurance, to help protect smallholder farmers from losses induced by climatic risks. Thus far, however, the number of successful crop insurance schemes targeting smallholders has been limited, arguably due to three main reasons: (1) high monitoring and verification costs of traditional indemnitybased insurance and, to an extent, of area-yield index-based insurance; (2) poor trust and high basis risk (i.e., imperfect correlation between farmers’ actual losses and insurance payouts) among farmers, leading to low demand for weather- and area-yield index-based insurance; and (3) the fact that insurance products often act as a substitute for resilience-enhancing technologies that could help increase profitability and prevent crop damage, such as irrigation, drought- or heat-tolerant cultivars, and integrated pest and disease management.

This project note describesthe potential for personalized remote advisory services bundled with insurance, provided based on crop pictures from farmers’ fields, to promote the adoption of resilienceenhancing technologies. Specifically, we present findings from a study in which we tested the feasibility and impact of including advisories in a picture-based insurance (PBI) product in India.

PBI verifies claims of crop damage by using a series pictures of insured fields from sowing to harvest. These pictures are taken by farmers themselves with a tamper-proof smartphone application. Cellphone imagery gives insurers “eyes on the ground”, reducing monitoring costs and allowing them to provide affordable and highquality crop insurance to smallholder farmers. Previous project notes have highlighted the feasibility and sustainability of this approach: PBI minimizes basis risk and improves trust and tangibility by relying on farmers’ direct engagement with the product.

To further improve the product’s value proposition and enhance its usefulness as a climate change adaptation tool, we complemented PBI with personalized remote advisories based on real-time observations of crop conditions using farmers’ pictures of insured plots. In the winter of 2017/18, we developed and implemented this advisory service in eight districts of Haryana and Punjab, India.

We hypothesize that the bundled service empowers data-driven farming through three channels. First, the stream of on-the-ground pictures allows experts to target crop management recommendations to each farmer’s individual situation, improving the value and timeliness of the advice when compared to traditional advisory services. Second, the tangibility of pictures, together with farmers’ improved engagement, can increase ownership and take-up of the advice and create benefits even in years without insurance payouts. Third, the direct real-time observation of field conditions can allow insurers to gather valuable, previously unavailable monitoring data and to provide recommendations to help prevent crop damage— potentially lowering expected insurance payouts and thus insurance premiums. Through these three channels, this approach can improve insurers’ competitiveness, boost the sustainability of the insurance product, and create a business case for advisory services.

This project note explores each of these three channels using the results of a formative evaluation of the personalized advisory service. Specifically, we focus on three questions: (1) Does the advisory TAKE-AWAY MESSAGES • Identifying crop growth stages and improving yield predictions based on smartphone pictures of fields is feasible and scalable, allowing advisory services to be tailored to individual farmer needs. • Personalized remote advisories based on these pictures (picture-based advisories or PBA) improve farmers’ knowledge of recommended practices. • Farmers report that such advisories help them reduce risk, suggesting that bundling picture-based insurance (PBI) with PBA can improve their adaptive capacity and help to lower insurance premiums. • Bundling PBA with PBI also improves farmer engagement in, satisfaction with, and willingness to pay for the advisories, suggesting strong complementarities between these services. P 2 service allow experts to target messages to a farmer’s individual situation, thus increasing the value and timeliness of the advice? (2) Does the tangibility of pictures and improved farmer engagement increase ownership and take-up of the advice? (3) Does this system provide insurers with real-time monitoring data that allow them to provide recommendations to minimize risk?

Methods and data

During the winter of 2017/18, we developed and rigorously tested an advisory service using a cluster randomized trial in approximately 200 villages in selected districts of Haryana and Punjab, India (Fatehgarh, Ludhiana, and Patiala in Punjab; Fatehabad, Karnal, Panipat, Sirsa, and Yamunanagar in Haryana). We randomly assigned villages to one of three interventions: in 50 villages, we broadcasted conventional interactive voice response (IVR) and SMS messages (control group); in 75 villages, we added personalized, picture-based advisory messages (PBA treatment); in the remaining 75 villages, we provided PBI coverage on top of the IVR, SMS, and PBA messages (PBA + PBI treatment).

In the control group, farmers who had access to a smartphone and had landholdings of less than 15 acres were invited to participate in the survey. In the two treatment groups, farmers complying with the same criteria were also invited to download a free app, Wheatcam, and to register in the app to receive the PBA messages. Farmers could enroll one of their fields by registering one or more “sites” in the smartphone app, provided that the pictures taken at one site could capture approximately one acre of their field. During the registration process, farmers had to send in an initial geo-tagged and time-stamped picture for each of their registered sites. A total of 1,779 farmers from 141 villages in Haryana and Punjab registered on WheatCam; of these, 76 percent enrolled one site, 18 percent enrolled two sites, and the remaining 6 percent enrolled more than two sites.

Repeat pictures

After taking the initial registration picture, farmers were asked to take three pictures per week throughout the entire growing season. Pictures needed to be taken between 10am and 2pm (in order to keep lighting conditions constant) from exactly the same location as the initial picture and with the same view angle every time. To facilitate this, the smartphone app made use of geo-tags to check whether the repeat picture was taken at the same location as the initial picture. In addition to providing visual aidsin the form of a line to mark the horizon, the app also provided a “ghost” image (a partially transparent image of the initial picture; see Figure 1) on the smartphone screen when a farmer was taking a repeat picture, allowing the farmer to align static features in the landscape (such as distant trees or structures) with those in the initial picture. The ghost image approach helped ensure an almost identical view frame throughout the season. Farmers then uploaded valid pictures to an online server.

Advisories and loss moment

All farmers in the study received conventional advisory messages built on CABI’s Direct2Farm program, which had been implemented in the area one year earlier, together with generic weather advisories for the area. Content was recorded and broadcast as IVR messages to all phone numbers registered in the study database; in addition, these messages were also sent out as SMS messages.

Farmers in the treatment groups (PBA and PBA + PBI) received additional personalized PBA messages, through either the app or SMS, when they submitted a repeat picture or contacted agronomic experts through the app. Four local agronomists interpreted the uploaded images (including close-up pictures of the field, requested by the app when a farmer declared to had suffered damage) and sent out personalized advisories based on cues visible in the pictures and additional sources of information such as weather data and regional pest monitoring. For this purpose, they used an online platform linked to the smartphone application that allowed them to accept or reject individual farmer’s pictures (according to whether the farmer respected the stated picture-taking protocol), review the images for visible cues to prompt specific crop management recommendations, and push remote advisories (PBA messages) directly through the app to each farmer’s phone. In addition, at the end of the season, these experts assessed the level of visible damage at each site using the time lapse of pictures. Assessments were made individually, and the median percentage of damage across experts was used as the final damage measure for that site. When large disagreement existed among individual assessments, we used the percentage of damage reached by consensus during a joint review.

In total, we received and analyzed 9,923 pictures and broadcast IVR and SMS messages to 32,237 wheat producers. A subsample of 1,179 beneficiaries received 5,081 personalized PBA messages, targeted to their individual needs.

Insurance

In January 2018, the 985 beneficiaries from randomly selected PBI villages received insurance to cover the sites they enrolled in WheatCam during the ongoing Rabi (winter) 2017/18 season against visible crop damage and extreme heat. Insurance was conditional on taking regular pictures of their plots using WheatCam. We told farmers that their insurance included coverage against damage visible in their pictures (PBI), as determined by experts during the loss assessments, as well as coverage against above-normal temperatures between January 21 and March 20, as measured at nearby weather stations. For farmers with more than 20 percent of assessed visible damage, we sent a damage report, including the pictures and the expert loss assessments, to HDFC Ergo General Insurance Company (henceforth HDFC), the project partner co-developing and underwriting the insurance product. HDFC then issued payments directly into farmers’ bank accounts.

For the 2017/18 season, the project used these expert assessments as a transparent, pragmatic solution to providing PBI coverage in the absence of automated tools to estimate damage from the time lapse of pictures. In the future, after additional training data is collected both for wheat and for other crops, we will develop image processing and machine-learning algorithms to automate the loss assessment process. Such automation will constitute an important public good to encourage the adoption of this approach at scale.

Evaluation data: Crop cutting and survey

At the end of the season, researchers visited 638 of the photographed plots to conduct crop cutting experiments for yield measurement. For each plot, the researchers sampled two different square meters that were visible in the pictures: one to the left and one to the right of the picture. The heads of the wheat plants falling inside these sampled square meters were threshed, the resulting grains were weighted, and the average weight from these two square meters was used to calculate yields per acre. We did not use these yield data as input in the loss assessments; the primary reason for collecting these data was to have an objective measure of yields, a critical step for assessing the validity of loss assessments from farmers’ own smartphone pictures.

For further validation and evaluation, we also interviewed a subsample of 529 households at endline (479 from the PBA and PBA + PBI villages and 50 farmers from the control group villages). We designed the endline survey to measure farmers’ knowledge and adoption of best practices (e.g., input use), damage suffered during the season, farmers’ satisfaction with the insurance product and advisory service, and farmers’ willingness to pay for PBA, PBI, and PBA + PBI.

Automated image processing

Finally, we explored to what extent crop pictures can be processed using automated procedures to monitor crop growth stages. For this component, described in more detail in Hufkens et al. (2018), we extracted greenness indices from smartphone pictures of wheat crops (taken during the 2016/17 season, in which we followed similar insurance procedures but did not provide advisories). We also added crop growth stage labels, indicating whether the crop in the picture was in the tillering phase (soil visible), stem extension phase (no soil or wheat ears visible), or heading and ripening phase (wheat ears visible). We used these data to analyze whether the greenness indices derived from a stream of pictures for a given plot were predictive of the growth stage on that plot.

Results

1. Does the advisory service allow experts to target messages to a farmer’s individual situation, increasing the value and timeliness of the advice?

Different growth stages imply exposure to different types of risk, and thus call for specific advisory messages. Real-time observations of crop growth stages based on farmers’ pictures could thus be used to provide more targeted messages and recommendations for weather- and non-weather-related risk management. As an initial step to gauge the potential for this, we used related research to analyze whether crop pictures could be used to infer important wheat crop growth stages (Hufkens et al. 2018). We found that greenness index curves derived from crop picturestaken throughout the entire season can indeed predict the transition from tillering to stem extension and from stem extension to heading and ripening with substantial accuracy (Figure 2). Moreover, the predictive power of using greenness indices derived from ground pictures was found to be higher than that of conventional satellite vegetation indices (Hufkens et al. 2018).

Figure 2: Growth stage and greenness over time

A separate approach tested whether integrating smartphone pictures into a convolutional neural network would improve yield prediction. These analyses were conducted by BKC WeatherSys, one of India’s first private sector meteorology and environmental technology companies and the first private sector entity in India to run numerical weather prediction models. Yields were indeed predicted with higher accuracy when smartphone pictures were included in their existing agronomic software that generates automated advisories (which presently combines crop models, weather data, and satellite imagery). Moving forward, we will partner with BKC WeatherSys to further explore the use of smartphone pictures to strengthen their existing advisory service.

2. Does PBA increase ownership and take-up of the advice?

We first tested farmers’ knowledge of practices, recommended through both the generic IVR/SMS and the PBA messages, at endline. Farmers in both the control group and the two treatment groups completed a knowledge test with five items. The personalized advisory messages increased farmers’ knowledge of these recommended agricultural practices by a statistically significant 78 percent, from an average of 0.64 correct answers in the control group to an average of 1.14 correct answers in the treatment groups (Figure 3). Interestingly, the difference in knowledge remained when we focused on the subset of questions related to information provided through both IVR and PBA (second set of bars) and through the PBA service alone (third set of bars).

This suggests that farmers incorporate content better through the PBA approach even when this content does not differ in essence from what is provided through the generic IVR messages, arguably related to a sense of increased ownership with this approach. Because treatment was randomized, we can attribute these effects to the PBA messages and conclude that personalized messages had stronger impacts on knowledge than generic IVR/SMS messages alone.

Figure 3:PBA improved knowledge of recommended practices

Furthermore, we analyzed the effect of receiving PBA on the adoption of herbicides, pesticides, and fungicides recommended through these messages (the endline survey did not include information on other recommended practices). Consistent with other studies on the impacts of agricultural advisory services (Aker, Gosh, and Burrell, 2016; Cole and Fernando, 2016), we found—despite positive effects on knowledge—no strong short-term effects on adoption of most management practices (Figure 4). The PBA messages did not have an effect on pesticide or weedicide use, although they resulted in a small but statistically significant reduction in recommended weedicide use.

Since farmers in this treatment group were only slightly more likely to suffer damage from weeding (with the difference between groups being not statistically significant), this suggests that the more personalized and targeted advice allowed farmers to economize on unneeded herbicide usage.

Figure 4: PBA did not have a strong effect on adoption of recommended practices

3. Does this system provide insurers with real-time monitoring data and allow them to provide recommendations to minimize risk?

In the long run, one could envision the advisories having stronger impacts on management practices, productivity, and profitability. In that case, the advisories, by generating direct real-time observations of field conditions, could allow not only advisory service providers but also insurers to gather valuable, previously unavailable monitoring data and to provide recommendations to potentially help prevent crop damage, thus lowering expected insurance payouts and insurance premiums.

To test this hypothesis, we asked farmers how recommendations received from alternative advisory sources had helped them minimize their crop risk. Even though most participants reported the PBA and IVR advisories as being helpful in minimizing risk, farmers actually receiving PBA seemed to differentially recognize the advantage of the project’s advisory sources over other regular sources—e.g., radio, TV, agri-dealer—of agricultural advice (Figure 5).

Although this is a subjective measure, it is encouraging to see that participants strongly valued the PBA messages in this regard. This suggests that the PBA messages were a more effective way to provide recommendations on agricultural risk management and could in the future be integrated into insurance products to lower expected payouts and thus premiums. From an advisory point of view, we also found strong complementarities of bundling PBA with PBI. Figure 6 shows that engagement in the PBA service—measured as the number of pictures submitted and farmers’ satisfaction—was significantly higher when bundled with PBI. Figure 7 provides further evidence of complementarities by comparing farmers’ willingness to pay for PBA and PBI when offered in isolation and when offered as a bundle, as measured during the endline survey. While willingness to pay for PBA alone was negligible, respondents were willing to pay an extra Rs. 125.9, equivalent to 8.7 percent of the insurance premium, when advisories were embedded in the PBI product.

Conclusions

We developed, implemented, and evaluated an innovative personalized advisory service that complements picture-based insurance (PBI), an easy-to-understand low-cost insurance product for visible crop damage. We sent personalized agricultural advice based on real-time observations of crop conditions, from sowing to harvest, from farmers’ pictures of their insured plots. Such a service can empower data-driven farming through three channels: experts can target messages to a farmer’s individual situation, thus increasing the value and timeliness of the advice; the tangibility of pictures increases ownership and take-up of the advice; and the service allows insurers to gather more monitoring data and provide recommendations on how to minimize risk, thus lowering expected insurance payouts.

We find that greenness indices derived from crop imagery can predict the onset of growth stages during which crops are more vulnerable to weather risk, outperforming satellite vegetation indices. Incorporating smartphone images improves the predictive power of existing automated advisory models, allowing for the provision of messages targeted to a farmer’s individual situation at scale. Survey data shows that advisory messages increased farmers’ knowledge on best agricultural practices, suggesting that the tangibility of pictures and the personalization of the advice increase ownership and can potentially serve to encourage take-up of the recommended practices. Finally, we find strong complementarities between PBA and PBI; farmers report that the PBA messages helped them reduce risk (potentially allowing for a reduction in insurance premiums), while farmer engagement in and willingness to pay for the PBA service significantly improved when bundled with PBI. This indicates strong synergies between insurance and advisories.

All in all, the findings from our formative evaluation point to the substantial advantages of combining picture-based insurance with advisory services. The synergies between insurance and advisory services could potentially enable insurance products to act as a complement to climate-smart resilience technologies, if advisories encourage the adoption of these technologies. Personalized advisories based on series of smartphone pictures taken by farmers can improve the effectiveness and encourage the uptake of agronomic recommendations, reducing agricultural risk and improving farmers’ adaptive capacity. Moreover, bundling PBI with advisories can increase the monitoring capacity of insurance providers and induce lower premiums in the long term, further adding to the attractiveness of the product.

Picture-based insurance: is it sustainable?

Effects on Willingness to Pay, Adverse Selection, and Moral Hazard

Picture-Based Crop Insurance (PBI) offers a new way of delivering affordable and easy-to-understand crop insurance, using farmers’ smartphone pictures to minimize the costs of loss verification. Millions of smallholder farmers lack access to affordable insurance because their farms are simply too small and too remote for insurers to affordably verify damage on insured crops. However, with improvements in technology, insurance companies may no longer need to send an insurance agent to verify a farmer’s claim in person. They could simply appraise losses by processing smartphone pictures of damaged crops, taken by farmers themselves, as long as these pictures reliably document crop damage due to a natural disaster and document that crops were managed appropriately until that event.

A previous project note (Kramer et al., 2017) [1] shows that farmers are able and willing to take such pictures, that crop damage is visible and quantifiable through pictures, and that picture-based loss assessment can capture damage that a weather index-based insurance product would not be able to detect. Thus, from a practical point of view, PBI seems to be a viable insurance approach that is worth developing further for implementation at a larger scale. However, insurance markets may fail not only because of high loss verification costs, but also because of low demand, adverse selection (farmers enrolling plots more prone to damage), and moral hazard (farmers reducing effort on their plots once insured). These factors could raise insurance premiums to unsustainable levels, crowding out demand.

This project note hence describes to what extent picture-based crop insurance is viable from an economic point of view, addressing the following questions: (1) Do farmers strategically reduce crop management efforts (that is, does PBI induce moral hazard) and is there evidence of tampering with pictures in order to receive payouts when they have PBI coverage? (2) What is farmers’ willingness to pay for PBI compared to willingness to pay for standard weather index-based insurance (WBI)? Is farmers’ demand for PBI strong enough to justify its higher costs? (3) To what extent do farmers selectively enroll plots that are more prone to damage? In other words, is PBI prone to adverse selection? We will answer these questions using the results from a formative evaluation of PBI in six districts of Haryana and Punjab, India.

Methods and data

We describe one study focusing on moral hazard, and one — using a sub-sample of farmers — focusing on demand and adverse selection.

Testing for moral hazard

A randomized trial with farmers from 50 villages in Haryana and Punjab, India randomly assigned villages to one of two treatment arms:

Within every village, 15 farmers completed a baseline survey in July 2016. These farmers were randomly selected from a list of all farmers within the village who satisfied the following criteria: (1) having less than 15 acres of operational farmland and (2) planning to grow at least two acres of wheat during the upcoming Rabi (winter) season. Of these invited farmers, 592 (approximately 12 farmers per village) agreed to regularly take pictures, using a smartphone app, of one acre of their wheat crop during the Rabi 2016-2017 season, in return for a monthly data plan and insurance coverage. Depending on the random assignment of treatment, these famers received either the WBI + pictures or WBI + PBI product for the one acre of wheat of which they were taking pictures.

Procedures for taking pictures were designed to be scalable, but in such a way that they would minimize moral hazard or potential tampering with pictures. To enroll, farmers took an initial overview picture of their plot, facing north. Farmers were asked to take three repeat pictures per week throughout the entire season, taken from the exact same location and with the same view angle as the initial picture. To facilitate this, the smartphone app included geo-tags and visual aids in the form of a “ghost” image (a partially transparent image of the initial picture) that allowed the farmer to align static features in the landscape (such as distant trees or structures) with those in the initial picture. Valid pictures were then uploaded to an online server and processed by the research team. Importantly, loss assessment relied on the time series of overview pictures instead of on snapshots zoomed in on damaged crops because the latter approach would be more susceptible to tampering.

One objective of the project is to develop automated image processing algorithms for loss assessment based on crop pictures. Since such algorithms were not yet available, at the end of the study season, an independent panel of wheat experts inspected the time series of pictures for visible damage due and not due to mismanagement of the crop. Experts assessed whether there had been any damage to the crop and, if damage had occurred, the percentage by which the crop had been damaged. Although these loss assessments were used to determine insurance payouts only for farmers with WBI + PBI coverage, experts also reviewed pictures from farmers with the WBI + pictures product (without knowing the type of coverage to which the farmer had been assigned). In the presence of moral hazard, we would expect observed crop damage to be more severe for farmers with PBI coverage (that is, for farmers with the WBI + PBI product).

In addition to the damage estimates, we rely on objectively measured yields and self-reported input use to test for moral hazard. Wheat yields were measured through crop cutting exercisesjust prior to harvest. In the presence of moral hazard, we would expect yields to be lower among farmers with the WBI + PBI product. Moreover, we look for evidence of reduced effort by farmers with PBI coverage by testing for lower usage of fertilizers, pesticides (including herbicides and fungicides), and farm labor. Farmers reported these input variables for the photographed plot during an endline survey just after harvest in May-June 2017.

Demand and adverse selection

During July 2017, several months before the start of the Rabi wheat production season (November-December), we conducted an additional study to understand other important aspects of product sustainability: the strength of demand for PBI and the degree of adverse selection into the product. To address these questions, the study elicited willingness to pay from a sub-sample of 100 farmers for four different products:

WBI only: offering coverage against excess rainfall and abovenormal temperatures, without having to take crop pictures.

WBI + pictures: the WBI product, but paying out only if the farmer regularly takes pictures of the insured plot.

WBI + PBI: the same product as WBI + pictures, but providing additional coverage against damage visible in the pictures.

PBI only: covering only against damage visible in the pictures

This design allows us to analyze several aspects of the demand for these products. One of our primary outcomes is the difference in willingness to pay between WBI only and WBI + PBI, which indicates how much farmers are willing to pay for extra PBI coverage. Other comparisons provide further insights regarding farmers’ perception of these products. The comparison of WBI + pictures and WBI only reveals farmers’ utility (or disutility) derived from having to take pictures regularly, providing an objective valuation of this implicit condition for receiving payouts from the PBI component. Comparing WBI + pictures and WBI + PBI quantifies farmers’ valuation of picture-based loss assessment conditional on taking pictures regularly; comparing WBI + PBI and PBI only indicates how much farmers value WBI coverage and whether demand for WBI + PBI could be enhanced by reducing or removing WBI coverage.

Farmers described their willingness to pay for a product that would cover one acre of Rabi wheat, and they had the freedom to choose which one plot to insure. By providing the farmer with this choice, we can compare the characteristics of plots that the farmer chose to enroll and plots that the farmer did not choose to enroll. These analyses provide information on the degree of adverse selection; that is, whether farmers selectively enrolled plots with an increased risk of damage, and hence increased chance for insurance pay-outs – for instance, plots poor access to irrigation or with poor soil quality. We will also test whether the amount that farmers are willing to pay extra for WBI + PBI compared with WBI only is higher for farmers whose crops are more likely to incur damage.

Willingness to pay was elicited using the Becker-Degroot-Marschak (BDM) method. Specifically, each farmer received a scratch card (see Figure 1 for an example) with illustrations of each product and – hidden under metallic scratch-off ink – a randomly assigned premium offer for a randomly selected product (e.g. Rs. 1,800 for WBI + PBI, in Figure 1). Farmers were instructed to write their maximum willingness to pay for each of the four products in the top panel before scratching off the ink to reveal the special offer. If a farmer’s willingness to pay for the selected product was at or above the pre- 3 mium offer, he would purchase the product at that premium. Otherwise, the farmer would not be able to purchase any of the products at that time. This gave farmers incentives to reveal their maximum willingness to pay, as writing down a lower amount could result in them losing out on a lower special premium and as stating a higher amount could result in them having to purchase a product at a premium that they were unwilling to pay.

Figure 1 – Scratch card to elicit willingness to pay

Results

1. Do farmers strategically put less effort into crop management when covered by PBI? Is there moral hazard?

The first question pertains to whether PBI induces moral hazard on insured plots. To that end, Figure 2 compares input usage for the Rabi 2016-2017 season (self-reported during the endline survey) for two types of farmers: those who received the WBI + pictures product and those who received the WBI + PBI product. If being covered against damage visible in pictures motivated farmers to put less effort into crop management, we would expect lower average input usage from farmers in the WBI+PBI group. However, Figure 2 shows that the usage of fertilizers (left chart), pesticides, fungicides, and herbicides (middle chart), and farm labor (right chart) is statistically indistinguishable between the two types of farmers. Thus, at the most direct level of input usage, we find no evidence of moral hazard.

Figure 2 – PBI coverage does not affect the quantity of inputs used

Figure 3 – PBI coverage does not affect yields or assessed damage

Interestingly, during focus group discussions, farmers reported that having to take pictures actually improved their management practices. Since they visited their field more often, they were able to monitor their crops more closely and detect weeds, pests, or or diseases at an earlier stage. Since both farmers with WBI + PBI coverage and those with only WBI coverage were required to take pictures, we cannot test this mechanism more formally, but this could be an area for future research.

It is possible that PBI induces moral hazard in ways not captured by input usage. Hence, we also test for moral hazard in a second set of outcome variables, which are objectively measured instead of self-reported. Figure 3 compares wheat yields (measured through crop cutting exercises) and damage due and not due to mismanagement (assessed by agronomic experts) for farmers with the WBI + pictures product and farmers with WBI + PBI product. Moral hazard would result in lower yields and higher damage for farmers with PBI coverage than for farmers taking pictures but not receiving PBI coverage. Nonetheless, we find no significant effect of PBI coverage on either yields or assessed damage, providing further evidence that farmers did not strategically worsen their crop management to receive insurance payouts

In sum, regardless of whether we analyze self-reported input usage, objectively measured yields, or damage assessed by agronomic experts, we find no evidence that farmers with PBI coverage reduce their efforts in response to having coverage against damage visible in pictures. Thus, at least in this specific context and during this first season, one of the main weaknesses often attributed to indemnitybased insurance — moral hazard — did not play a role.

2. Do farmers prefer PBI coverage over WBI coverage?

If so, by how much? The second question pertains to whether farmers’ valuation of PBI is sufficiently high to justify its additional costs. Figure 4 presents the average willingness to pay for each of the four products – that is, the average amount that farmers wrote down as the maximum amount they would pay for the four products on the scratch card.

Figure 4 shows that farmers are willing to pay Rs. 736 for the WBI only product (left bar). Interestingly, the willingness to pay for the WBI + pictures product — which requires farmers to take pictures of their crops regularly throughout the Rabi season — was only Rs. 21 lower, which is not a statistically significant difference. Contrary to our expectations, having to take pictures of their crops on a regular basis did not reduce farmers’ willingness to pay by a significant amount. On the other hand, farmers were willing to pay Rs. 866 on average for the PBI only product, which is a significant Rs. 129 higher than their willingness to pay for the WBI only product. Finally, the WBI + PBI product, which bundles WBI and PBI coverage, increased willingness to pay by a significant Rs. 338 (to Rs. 1,052).

It is important to compare farmers’ willingness to pay for these different products with the premium at which the product would be offered under real market conditions. The average willingness to pay for WBI only is only 21.2 percent of the actual insurance premium of Rs. 3,473. In addition, the insurance premium for the WBI + PBI product was Rs. 660 higher than willingness to pay, at Rs. 4,133. Thus, the average farmer also does not seem willing to pay the full cost of the additional PBI coverage. It is important to note, however, that without historical data to price the PBI product, this Rs. 660 included an additional uncertainty premium. In addition, wheat yields are relatively stable in Haryana and Punjab, which could further reduce the premium mark-up. Thus, in our study area, Rs. 660 is most likely an upper bound of the amount by which the insurance premium should increase when adding PBI coverage to a WBI product.

An alternative way of presenting farmers’ valuation of PBI is by summarizing the payout that a farmer expects to receive on average per year from a given insurance product. After eliciting their willingness to pay, we asked farmers to indicate the probability of incurring damage due to extreme heat, excess rainfall, or lodging; the probability of the product to pay out in such an event; and the expected payout conditional on receiving a payout. We also asked them to indicate the probability of the product paying out if there was no damage and how much the product would pay in that case. This allows us to construct an expected average payout, summarized for the median farmer by the green bars in Figure 5. For WBI only, the median farmer expected to receive Rs. 720; for PBI only, this is Rs. 1040, while for WBI + PBI, the expected payout is Rs. 1800.

The findings are interesting and perhaps somewhat surprising. Farmers on average expect to receive much higher payouts, especially for picture-based coverage added to a WBI product, than what they are willing to pay. This suggests that the median farmer would not be willing to take insurance at premiums that would be required to meet that farmer’s expectations. At the same time, this premium calculated based on farmers’ expectations could be interpreted as the extent to which farmers value the product. Given that farmers expect much higher payouts on average under WBI + PBI compared to WBI only and PBI only, this could mean that farmers really value the bundled product but that other constraints prevent them from paying the full premium required to receive such high payouts on average per year.

What do these findings mean? First, farmers’ demand for PBI is stronger than for WBI, and having to take pictures does not appear to be a major barrier to enroll. Second, farmers also expect to receive higher payouts and more complete insurance from a product that includes PBI. However, their willingness to pay remains too low to market the product under the current conditions. Part of this might be related to the timing of the willingness to pay elicitation; farmers were surveyed in July, four months before land preparation for the wheat crop. Thus, risk in wheat production may not have been very salient at this time, and farmers may have preferred to wait until after the Kharif harvest before deciding to purchase insurance for the next season.

Nevertheless, in the absence of premium subsidies for PBI, it would be important to further improve farmers’ willingness to pay. One potential way of doing so could be through insurance education, specifically by discussing with farmers their expectations of the product’s payouts and how much the premium associated with that average payout would need to be for the product to be sustainable. This seems especially important for the bundled product, for which farmers expect double the payouts but are not willing to pay double the premium.

Another potential channel for improvement is to design premium collection in such a way that liquidity constraints do not depress demand. We find that wealthier farmers and farmers who took out a loan in the previous season (which reflects, among other things, better access to credit) are willing to pay nearly Rs. 300 more for insurance than farmers who had not taken out a loan. This suggests that liquidity constraints reduce willingness to pay. In future work, it will be important to address such liquidity constraints, for instance by deferring premium payments until the end of the season.

3. Do farmers selectively enroll plots into PBI? Is there adverse selection?

The final question addressed in this project note relates to adverse selection, or the tendency for insurance to be purchased by farmers who are most at risk (who value it the most, since they expect the highest payouts) and for their most vulnerable plots, at the expense of farmers and plots at risk of the least damage (and hence the lowest insurance payouts). This tendency would induce insurance companies to raise premiums and further crowd out farmers and plots at lower risk, creating a feedback loop that could make the product unsustainable.

We study two aspects of adverse selection: at the farmer level and at the plot level. For the former, we test whether farmers with worse yields and farmers for whom experts detected damage in the crop pictures (arguably the riskier farmers) are willing to pay relatively more for PBI insurance coverage. For the latter aspect, given that farmers had to select one plot to be insured, we test whether they tend to select plots with worse (i.e. riskier) characteristics.

First, we test whether there is a correlation between–on one hand—a farmer’s yields and the degree of damage visible in the farmer’s pictures and—on the other hand—the amount that a farmer is willing to pay extra to purchase WBI + PBI instead of WBI only. In the presence of adverse selection, this extra willingness to pay would be higher for farmers with a higher chance of insurance payouts, that is, farmers with lower yields or more severe damage. However, the willingness to pay for extra coverage does not decrease based on a farmer’s yields and is also not significantly higher for farmers with visible damage in their pictures (Figure 6). Thus, we find no evidence of adverse selection in the willingness to pay for extra PBI coverage.

Figure 6 – Willingness to pay for extra PBI coverage is not predicted by past yields or assessed damage

As a next step, we test whether the plots that farmers choose to enroll in insurance are of lower quality, or of higher risk, than the plots that they choose not to enroll in insurance. To that end, Figure 7 shows comparisons of various quality indicators between the plot that the farmer selected to enroll in insurance and the other plots that the farmer opted not to enroll. Across a number of important characteristics related to a plot’s riskiness – such as how far the plot is from an irrigation source, previous year’s yields, the plot’s sales and rental value per acre, whether the plot has good drainage, and whether the plot has good soil fertility – we find no quality differences between those plots selected and those not selected for PBI coverage. Thus, at the plot level, we also find no evidence of adverse selection.

Figure 7 – The plot that the farmer chooses to enroll has the same quality as his other plots

Conclusions

In conclusion, PBI is a promising approach to reduce the costs of loss verification and to improve product ownership among smallholder farmers, by relying on pictures from inexpensive smartphone cameras taken by farmers themselves. Farmer expectations about PBI payouts, especially during bad years, indicate that they believe this product is able capture damage considerably better than weatherindex based alternatives. As a result, they are willing to pay more for PBI than for more traditional WBI products. In addition, farmers are willing to pay more for PBI coverage than the hypothetical premiums calculated from the average payouts made in Rabi 2016-2017 season, suggesting that the product could be offered sustainably in real market conditions if true premiums were adjusted for actual losses (plus a reasonable loading factor). Moreover, the need for farmers take pictures on a regular basis was not a major factor determining their willingness to pay, suggesting that this approach is indeed feasible.

At the same time, while willingness to pay for the bundled product that combines WBI and PBI coverage is considerably higher than that for the WBI coverage alone, it remains far below the hypothetical insurance premium implicit in farmers’ expectations about payouts. On the one hand, this means that farmers see the value of a picture-based insurance approach. On the other hand, they are either not willing or not able to pay the extra costs associated with such insurance. Solutions that help relax liquidity constraints (for instance, deferred premium payments or bundling with loans) and insurance education may help better align farmers’ willingness to pay with insurance premiums.

Multi-peril indemnity insurance products that cover farmers against actual damage, as opposed to pre-specified weather conditions, are often considered to be subject to moral hazard and adverse selection; however, we find no evidence that the PBI approach induces either. This could indeed be a positive consequence of farmers regularly taking pictures, which may give them the impression that the insurance company is watching them and that they cannot reduce efforts or tamper with the pictures to trigger insurance payouts without being noticed. One caveat worth mentioning in this regard is that as farmers become more acquainted with the product, moral hazard and adverse selection may arise over time. Testing for such mechanisms remains important in the monitoring and evaluation of PBI approaches.

Take-away messages

Farmers are willing to pay more for picture-based insurance (PBI) than for weather index-based insurance (WBI).

[2] Since this measure captures variability in only one season, the actual — but unknown — expected payout may deviate from this, especially for weather, which is a covariate risk that varies more over time than over space (at least in our study, which included only six districts from two states). It would be expected, though, that especially for the PBI add-on, the average payout plus 30 percent loading could be a reasonable proxy, because the added value of PBI is mostly in observing localized risks such as hail and lodging that are more idiosyncratic in nature.

Picture-based crop insurance: is it feasible?

Using farmers’ smartphone pictures to minimize the costs of loss verification

The Picture-Based Crop Insurance (PBI) project aims to develop a new way of delivering affordable and easy-to-understand crop insurance using farmers’ smartphone pictures to minimize the costs of loss verification. The project, funded by the CGIAR Research Program on Policies, Institutions, and Markets(PIM), is a partnership between the International Food Policy Research Institute (IFPRI), the Borlaug Institute for South Asia (BISA), HDFC Ergo General Insurance, Limited, and researchers from The George Washington University, Boston University, and Ghent University.

Millions of smallholder farmers lack access to affordable insurance – their farms are simply too small and too remote for insurers to affordably verify damage on insured farmers’ crops. By taking regular pictures using their own smartphones, farmers can reliably document damage after a natural calamity and provide evidence that the crop was managed appropriately until that point. This brings down the costs of loss verification substantially. Instead of sending an insurance agent to verify a farmer’s claim, insurance companies can appraise losses by simply processing the smartphone pictures, and can even rely on advances in image processing techniques to help automate the loss assessment procedure. In other words, PBI could directly provide insurers with eyes on the ground, at limited cost. While the potential for such a system is vast, it is critical to first assess its feasibility.

The PBI project has been underway since 2015 to address the following questions: (1) Can farmers take regular and consistent pictures of their fields using their own smartphones for loss assessment purposes? (2) To what extent is damage visible and quantifiable in smartphone pictures, and what types of damage are visible? (3) Does picture-based loss assessment capture damage that weather index-based insurance products do not capture? (4) Do farmers strategically reduce efforts or tamper with pictures to receive payouts when they have PBI coverage (in other words, does PBI induce moral hazard)? (5) Does PBI increase the demand for crop insurance?

This project note describes the results from a formative evaluation of PBI in six districts of Haryana and Punjab, India. Here, we focus on the first three questions, which all relate to measuring damage using smartphone pictures. These are more general questions that not only have implications for the design of insurance products but are also of interest to other institutions interested in measuring crop damage (for instance, statistical agencies and agro-advisory service providers). The last two questions are relevant mainly in an insurance context and will be discussed in a separate project note (forthcoming).

Methods and data

The study focused on villages near 25 weather stations in selected districts of Haryana and Punjab, India (Fatehgarh, Ludhiana, and Patiala in Punjab; Fatehabad, Sirsa, and Yamunanagar in Haryana). Specifically, within a radius of five kilometers from each weather station, two villages were randomly selected and 15 farmers per village were invited to participate. These farmers were randomly selected from a list of all farmers within the village who satisfied the following criteria: (1) having less than 15 acres of operational farmland and (2) planning to grow at least two acres of wheat during the upcoming Rabi season.

Of these invited farmers, 592 (approximately 12 farmers per village) agreed to participate in the PBI study. In October 2016, participating farmers received insurance to cover one acre of their wheat crop during the upcoming Rabi (winter) 2016-2017 season. Insurance was conditional on taking regular pictures of their plots from sowing to harvest using WheatCam, a smartphone app developed for the project. In addition, data plans were provided for farmers to upload these pictures. Farmers were told that their insurance included coverage against damage visible in their WheatCam pictures (PBI) and coverage against excess rainfall and above-normal temperatures between February and April.

Overview of procedures

Before cultivation: The farmer downloads the app, enrolls in insurance, and takes an initial overview picture of the insured site, including identifiable objects in the background. The app uploads the picture with its location.

During cultivation: Every few days, the farmer takes geotagged pictures with the same view as the initial picture, in addition to close-up pictures when the crop has suffered any damage.

After cultivation: All pictures (including both the time series of overview pictures and the close-up pictures) are analyzed along with auxiliary data to verify losses.

After loss verification: Farmers for whom the pictures or auxiliary data show damage are sent an insurance payout

To enroll, farmers took an initial overview picture of their plot, facing north, with an identifiable object in the background (for this season, a reference pole placed in the cultivated field was used, although alternative objects, such as trees or buildings on the horizon, could be considered to fix the reference frame). Farmers also received a low-cost auxiliary pole that acted as a tripod upon which to place the phone; this helped fix the position for taking repeat pictures

Repeat pictures

After taking the initial picture and throughout the entire season, farmers were asked to take three repeat pictures per week between 10am and 2pm (in order to preserve lighting conditions), from the exact same location as the initial picture and with the same view angle every time.

To facilitate this, the smartphone app included geotags to check whether the repeat picture was taken at the same location as the initial picture; it also provided visual aids in the form of a “ghost” image (a partially transparent image of the initial picture) that allowed the farmer to align static features in the landscape (such as distant trees or structures) and the reference pole with those in the initial picture, thus ensuring an almost identical view frame throughout the season. Valid pictures were then uploaded to an online server and processed by the research team.

Loss assessment

At the end of the season, an independent panel of wheat experts evaluated the time series of pictures for each farmer. They assessed whether there had been any damage to the crop and, if damage had occurred, they determined the percentage by which the crop had been damaged. Assessments were first done individually. When large disagreement existed between the different experts’ assessments, experts would jointly review a case and agree upon a final damage assessment; otherwise the median assessment across experts was considered for insurance payouts. For farmers with more than 20 percent of assessed damage, a damage report including the pictures and the expert loss assessments was sent to HDFC, the project partner underwriting the insurance product, to issue payments directly into farmers’ bank accounts.

For the 2016-2017 season, the project used these expert assessments as a transparent, pragmatic solution to providing PBI coverage in the absence of automated tools to estimate damage from the time series of pictures. One of the future objectives of the project is to develop an image processing algorithm using the data from the 2016-2017 evaluation and subsequent seasons to automate loss assessment. Collecting more imagery and training data to develop these algorithms not only for wheat but also for different crops is an important next step. The end result from these data collection efforts will constitute a valuable public good which private insurance companies will be able to use to improve their products.

Crop cutting experiments

At the end of the season, researchers visited the photographed plots to conduct crop cutting experiments for yield measurement. For each plot, the researchers sampled two different square meters that were visible in the pictures: one to the left of the reference pole and one to the right of the reference pole. The heads of the wheat plants falling inside these sampled square meters were threshed, the resulting grains were weighted, and the average weight from these two square meters was used to calculate yields per acre. These yield data were not used as input in the loss assessments; the primary reason for collecting these data was to have an objective measure of yields, a critical step for assessing the validity of loss assessments based on farmers’ own smartphone pictures.

Results

1. Can farmers provide a time series of pictures to be used in loss assessment?

The first prerequisite for such an insurance system to be feasible is that farmers be willing and able to take pictures of their fields regularly and with a sufficient level of quality. Out of the 592 farmers encouraged to take pictures, 475 farmers (80.2 percent) uploaded at least one valid picture during the season. Of the farmers who took pictures, the large majority (more than 83 percent) took at least six pictures throughout the season—or roughly one picture per month— while more than 59 percent of them took pictures twice a month or more.

Greenness levels and other features of the pictures are most comparable across time when lighting conditions are held constant. This is why farmers were asked to take pictures between 10am and 2pm; however, if they were unable to make it in that window, the app allowed them to take a picture at a different time. Farmers took pictures across a broader range of times than initially requested, in part because most plots are not located close to the farmer’s house, meaning that they generally visit their plots only at certain times of the day. Loss assessment algorithms will have to factor in this ground reality. Finally, we present the number of farmers who took at least one picture in a given calendar week throughout the season.

The pattern is encouraging, with sustained submissions from an average of 200 farmers weekly, except for the beginning of the season (when the wheat plants had not started growing yet and when farmers were facing technical challenges with the app) and the post-harvest period (when farmers no longer had to take pictures).

Figure 3: Number of uploaded pictures for farmers who took pictures

Figure 4: Number of pictures by time of the day

Figure 5: Number of farmers uploading at least onepicture by week

In summary, while farmers did not strictly follow the requested protocol, they were able to submit a substantial number of pictures for loss assessment. Moving forward, a more flexible protocol for the number of pictures required per week will be needed, especially early in the season when the crop is not showing yet. It could also be important to make the benefits of taking pictures more salient to farmers, as they may not see the direct value of doing so in the absence of damage. In this regard, bundling the insurance product with other services, such as agro-advisory services(for instance, irrigation advice) and pest detection could help boost farmers’ interest in taking pictures more regularly, which will improve outputs from loss assessment. Overall, however, farmers appear able and willing to take pictures for loss assessment purposes themselves

2. To what extent is damage visible in pictures? What type of damage is visible?

The second prerequisite entails that damage arising from different types of hazards be plainly visible in a picture taken at a distance of a few meters. Initial conversations with local wheat agronomists indicated that pictures would be able to capture most — though not all — hazards. Certain events such as lodging (the bending of the wheat plant due to winds and wet, loose soil), hail, or certain common wheat diseases such as yellow rust would indeed be visible. Other events, such as blight or high temperatures, which can affect grain filling without showing a direct effect on the external aspect of the plant, would be much more difficult to identify. Farmers’ perceptions measured through surveys were in agreement with this. To cover farmers from high temperatures, we combined picture-based insurance with a weather-index based insurance product. Figure 6 shows a box plot of the loss assessments, ordered by the median assessment within a site. A few interesting patterns can be seen from this figure. The level of agreement between experts is quite high for low levels of damage (under 20 percent). Even for sites with more severe damage (over 20 percent), most experts agree over the approximate region in which the damage occurs.

These results are indicative of crop experts being able to identify crop losses from direct visual inspection of pictures.The above results could potentially be improved through a loss detection algorithm that considers not only the pictures themselves but also the development of greenness and texture indices over time, as well as close-up pictures and localized weather information. Texture indices can measure how upright the crop is and are thereby a potential way to capture lodging and hail storms occurring too late in the growing season for the crop to recover. Coarser low-cost satellite imagery currently available would be unable to detect such events, and even higher-resolution microsatellites may lack the advantage of close-up ground-based view angles. In addition, microsatellite imagery is less well-suited for the insurance problem at hand due to its cost, visibility issues (i.e. clouds), and reduced interpretability of the images.

Figure 7: Yields for farmers in different PBI payout categories

It is important to note that for insurance purposes, it is not necessary to translate pictures into precise estimates of crop damage, particularly at very low levels of damage (when insurance does not pay out). As a final way to assess whether the pictures contain sufficient information for loss assessment, we compare yields – measured through crop cutting experiments – for farmers with different levels of insurance payouts (median expert loss assessment below 5 20 percent, between 20 and 50 percent, and above 50 percent).

Figure 7 shows the average yield for farmers without a PBI claim to be around 20 quintals per acre, with a significantly lower average yield of 18 and 10 quintals per acre for, respectively, farmers in the first and second or third PBI payout categories. In other words, through visual inspection of the pictures, experts were able to accurately identify yield losses at a scale that is relevant for insurance purposes.

3. How does PBI compare to index insurance?

A final question is whether picture-based loss assessment adds value to existing cost-effective ways of estimating crop damage. An alternative that has gained popularity over the past decades is weatherbased index insurance, which bases its payouts on the value of an index measured from a weather station. Removing the need for farmer’s involvement and the back-end system necessary for PBI, this alternative is certainly cheaper than traditional indemnity insurance; however, it measures weather at a location often several kilometers away from a farmer’s plot and depends on identifying an appropriate relationship between weather and crop growth, which may not always reflect a farmer’s true losses. Here, we compare the performance of picture-based loss assessment with that of a weather index-based assessment in order to understand whether the additional costs of a PBI approach can be justified.

The weather-index based insurance (WBI) product covered the insured farmer from higher-than-normal night temperatures and (unseasonal) excess rainfall at the end of the wheat season, from February to April. The indices relied on daily minimum temperature and rainfall collected at 25 nearby weather stations (located within five kilometers of the study villages) and were developed based on Focus Group Discussions with farmers and Key Informant Interviews with local wheat agronomists. For each index, triggers were rounded values of the 70th percentiles in historical records for one weather station in Haryana and one weather station in Punjab.

Figure 8: Yields for farmers without and with WBIpayouts

Figure 8 compares the average yields from crop cutting experiments across farmers who would and would have not received payouts from this WBI product. Given the effort and care that was put into designing the weather index-based product, the result is very disappointing. The yields from farmers for whom WBI would have triggered a payout are virtually indistinguishable from those for whom no payout would have been triggered. This is indicative of a very large degree of overall basis risk, where actual losses do not correspond with insurance payouts. Having to pay farmers in years that they have normal yields will be reflected in the price of the insurance product. At the same time, these results indicate the value that picture-based loss assessment can have by reducing the degree of basis risk characteristic of more standard WBI products.

Conclusions

Picture-based crop insurance is a new approach to improve smallholder farmers’ access to affordable but high-quality insurance. By leveraging increasing smartphone ownership among smallholder farmers, and relying on automated image processing techniques, the goal of PBI is to combine key advantages of index insurance – fast and inexpensive claims processing – with those of indemnity insurance – low basis risk and easy-to-understand products. The picture-based insurance project is the first formative evaluation of this potentially game-changing insurance approach.

Based on the formative evaluation of this novel PBI product, we find that farmers are largely able to follow picture-taking protocols. Further, agronomists and farmers agree that the most important risks in wheat production are plainly visible in pictures, and experts are indeed able to detect such damage in the pictures. Moreover, picture-based insurance payouts are better correlated with yields than weather-index based insurance payouts, indicating that the pictures captured crop damage better than the indices behind the weather index-based insurance product — despite careful efforts in designing this latter product.

The study also highlights areas for further research. First, although farmers are able and willing to take enough pictures for loss assessments, there is room for improvement. In the formative evaluation, communication was one-way, from the farmer to the project. Future efforts could concentrate on bundling picture-based insurance with picture-based agro-advisory or pest-detection service in order to make the process more inclusive and make the benefits of taking pictures more salient to farmers.

Second, loss verification through agronomic experts is not an economically viable model in the long run; automated image processing algorithms need to be developed to lower the costs of loss assessment through pictures. This formative evaluation collected valuable initial data to develop such algorithms, although more data will be required to improve performance. In this regard, research efforts to collect pictures of crops along with measures of damage and yields can prove to be very valuable.

Third, this note focused on the technical feasibility of PBI. A second project note describes in detail the feasibility of the scheme from an economic point of view. That note addresses two questions raised earlier: 1. Do farmers strategically reduce efforts or tamper with pictures to receive payouts when they have PBI coverage (in other words, does PBI induce moral hazard)? 2. Does PBI increase the demand for crop insurance? Formative evaluation results are promising in this respect and highlight the costs and benefits of the PBI approach.

PBI has the potential to bring about important changes in the way that insurance is offered to smallholder farmers. Our first results indicate that this is a promising approach that will help protect farmers from the increasing risk of extreme natural hazards and calamities posed by climate change. Being able to rely on this protection could help farmers better invest in their farms, improve their yields, become more resilient, and move toward a better future.

Take-away messages

Farmers are able and willing to take enough pictures of sufficient quality for loss assessment. Bundling this with agro-advisory services can potentially improve compliance.

Damage is visible from smartphone pictures and can be quantified by agronomic experts. This paves the way for algorithms that automate loss assessment procedures.

Picture-based loss assessments are strongly correlated with yields and improve upon weather index-based measures that were carefully designed to capture damage.

Disclaimer

The following article has been previously published. The GBG Fund makes no proprietorial claim to content and claims no editorial responsibility. The article appears here with the kind permission of the author and the original publisher.